AI LEAD

Greeting from Linnk Group

Title:

About Us:

Linnk Group is a global STEM recruitment and technology consultancy with established entities in the UAE, KSA, Qatar, India, and the UK. We are building AI-first platforms that power intelligent automation across recruitment, sales, and workforce management. Our technology stack includes LLM-based applications, RAG pipelines, CV parsing engines, chatbots, and agentic AI workflows integrated with cloud-native infrastructure.

We are looking for a hands-on and experienced Lead AI/ML Engineer to architect, build, and scale our GenAI systems across products. This is a foundational role that will help define the technical direction and lead the implementation of production-grade AI components.

deep learning and machine learning architectures,

• In-depth understanding of

  Transformers

  and modern

  LLM architectures

  (e.g.,

  LLaMA

  ,

  Mistral

  ,

  Qwen

  ,

  GPT

  ,

  Claude

  ,

  BERT

  ), with a focus on

  NLP

  and

  generative AI

  . Familiarity with legacy models like

  RNNs

  and

  LSTMs

  is a plus.
• Expertise in

  Machine Learning (ML)

  and

  Natural Language Processing (NLP)

  frameworks such as

  PyTorch, Tensorflow, Hugging Face Transformers, LangChain

  .
• Proven experience designing

  recommendation systems

  and

  LLM-based applications

  , including

  RAG pipelines

  , structured generation, and retrieval systems.
• Skilled in

  model performance optimization

  , including quantization, pruning, latency reduction, and memory-efficient inference.
• Advanced knowledge of

  embeddings

  and

  vector search

  using tools like

  FAISS, Milvus, Pinecone, and Quadrant

  .
• Hands-on experience with

  CI/CD pipelines

  for ML/LLM workflows (e.g., GitHub Actions, Jenkins).
• In-depth understanding of

  MLOps and LLMOps

  practices including model versioning, deployment automation, drift detection, and continuous monitoring.
• Familiarity with

  automated evaluation frameworks.

• Skilled in

  Docker, Kubernetes

  , and building scalable microservices.
• Demonstrated experience deploying and maintaining ML systems on

  AWS

  , including

  SageMaker, EC2, EKS, RDS, and S3

  .
• Ability to optimize and scale

  LLM inference endpoints

  , and orchestrate

  multi-agent workflows

  in production environments.
• Deep understanding of

  vector databases

  ,

  semantic search

  , and

  dense retrieval techniques

  for real-time matching.

AI agent frameworks